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Nonlinear Model Predictive Control of a Conductance-Based Neuron Model via Data-Driven Forecasting (2312.14274v2)
Published 21 Dec 2023 in q-bio.NC, cs.SY, and eess.SY
Abstract: Objective. Precise control of neural systems is essential to experimental investigations of how the brain controls behavior and holds the potential for therapeutic manipulations to correct aberrant network states. Model predictive control, which employs a dynamical model of the system to find optimal control inputs, has promise for dealing with the nonlinear dynamics, high levels of exogenous noise, and limited information about unmeasured states and parameters that are common in a wide range of neural systems. However, the challenge still remains of selecting the right model, constraining its parameters, and synchronizing to the neural system. Approach. As a proof of principle, we used recent advances in data-driven forecasting to construct a nonlinear machine-learning model of a Hodgkin-Huxley type neuron when only the membrane voltage is observable and there are an unknown number of intrinsic currents. Main Results. We show that this approach is able to learn the dynamics of different neuron types and can be used with MPC to force the neuron to engage in arbitrary, researcher-defined spiking behaviors. Significance. To the best of our knowledge, this is the first application of nonlinear MPC of a conductance-based model where there is only realistically limited information about unobservable states and parameters.
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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. 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Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. 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Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. 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Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Newman, J.P., Fong, M.-f., Millard, D.C., Whitmire, C.J., Stanley, G.B., Potter, S.M.: Optogenetic feedback control of neural activity. eLife 4, 07192 (2015) https://doi.org/10.7554/eLife.07192 Afram and Janabi-Sharifi [2014] Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. 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IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. 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[2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. 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Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. 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IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. 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[2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. 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[2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Newman, J.P., Fong, M.-f., Millard, D.C., Whitmire, C.J., Stanley, G.B., Potter, S.M.: Optogenetic feedback control of neural activity. eLife 4, 07192 (2015) https://doi.org/10.7554/eLife.07192 Afram and Janabi-Sharifi [2014] Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. 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IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. 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[2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Newman, J.P., Fong, M.-f., Millard, D.C., Whitmire, C.J., Stanley, G.B., Potter, S.M.: Optogenetic feedback control of neural activity. eLife 4, 07192 (2015) https://doi.org/10.7554/eLife.07192 Afram and Janabi-Sharifi [2014] Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. 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[2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. 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IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. 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[2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. 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[2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Newman, J.P., Fong, M.-f., Millard, D.C., Whitmire, C.J., Stanley, G.B., Potter, S.M.: Optogenetic feedback control of neural activity. eLife 4, 07192 (2015) https://doi.org/10.7554/eLife.07192 Afram and Janabi-Sharifi [2014] Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. 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[2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. 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[2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. 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[2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. 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[2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. 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MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. 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Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. 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[2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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[2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. 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[2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. 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Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. 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[2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment 72, 343–355 (2014) https://doi.org/10.1016/j.buildenv.2013.11.016 Zhao and Guo [2022] Zhao, C., Guo, L.: Towards a theoretical foundation of PID control for uncertain nonlinear systems. Automatica 142, 110360 (2022) https://doi.org/10.1016/j.automatica.2022.110360 Johnston and Wu [1995] Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Johnston, D., Wu, S.M.-s.: Foundations of Cellular Neurophysiology. MIT Press, Cambridge, Mass (1995) Bjoring and Meliza [2019] Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. 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Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. 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Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bjoring, M.C., Meliza, C.D.: A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex. PLOS Computational Biology 15(1), 1006723 (2019) https://doi.org/10.1371/journal.pcbi.1006723 Chen and Meliza [2018] Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. 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Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. 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Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. 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[2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chen, A.N., Meliza, C.D.: Phasic and tonic cell types in the zebra finch auditory caudal mesopallium. Journal of Neurophysiology 119(3), 1127–1139 (2018) https://doi.org/10.1152/jn.00694.2017 Martinez et al. [2023] Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Martinez, S., Garcia-Violini, D., Belluscio, M., Piriz, J., Sanchez-Pena, R.: Dynamical models in neuroscience from a closed-loop control perspective. IEEE Reviews in Biomedical Engineering 16, 706–721 (2023) https://doi.org/10.1109/RBME.2022.3180559 Hewing et al. [2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. 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International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. 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Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. 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Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. 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[2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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[2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2020] Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Hewing, L., Wabersich, K.P., Menner, M., Zeilinger, M.N.: Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems 3(1), 269–296 (2020) https://doi.org/10.1146/annurev-control-090419-075625 Holkar and Waghmare [2010] Holkar, K., Waghmare, L.M.: An overview of model predictive control. International Journal of Control and Automation 3(4), 47–63 (2010) Lin et al. [2023] Lin, L., Oncken, J., Agarwal, V., Permann, C., Gribok, A., McJunkin, T., Eggers, S., Boring, R.: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system. Progress in Nuclear Energy 156, 104527 (2023) https://doi.org/10.1016/j.pnucene.2022.104527 Raković and Levine [2019] Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. 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Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. 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Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. 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IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Raković, S.V., Levine, W.S. (eds.): Handbook of Model Predictive Control. Control Engineering. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-77489-3 Brunton and Kutz [2019] Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. 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Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, Cambridge (2019) Schwenzer et al. [2021] Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Schwenzer, M., Ay, M., Bergs, T., Abel, D.: Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology 117(5-6), 1327–1349 (2021) https://doi.org/10.1007/s00170-021-07682-3 Rabinovich et al. [2006] Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. 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Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. 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Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. 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Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I.: Dynamical principles in neuroscience. Reviews of Modern Physics 78(4), 1213–1265 (2006) https://doi.org/10.1103/RevModPhys.78.1213 Toth et al. [2011] Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. 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[2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Toth, B.A., Kostuk, M., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties I: variational methods. Biological Cybernetics 105(3-4), 217–237 (2011) https://doi.org/10.1007/s00422-011-0459-1 Fröhlich and Jezernik [2005] Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fröhlich, F., Jezernik, S.: Feedback control of Hodgkin–Huxley nerve cell dynamics. Control Engineering Practice 13(9), 1195–1206 (2005) https://doi.org/10.1016/j.conengprac.2004.10.008 Yue et al. [2022] Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Yue, R., Tomastik, R., Dutta, A.: Non-linear model-based control of neural cell dynamics. preprint, In Review (May 2022). https://doi.org/10.21203/rs.3.rs-580874/v2 Senthilvelmurugan and Subbian [2023] Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. 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[2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Senthilvelmurugan, N.N., Subbian, S.: Active fault tolerant deep brain stimulator for epilepsy using deep neural network. Biomedical Engineering / Biomedizinische Technik 68(4), 373–392 (2023) https://doi.org/10.1515/bmt-2021-0302 Kostuk et al. [2012] Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kostuk, M., Toth, B.A., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods. Biological Cybernetics 106(3), 155–167 (2012) https://doi.org/10.1007/s00422-012-0487-5 Ullah and Schiff [2009] Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Ullah, G., Schiff, S.J.: Tracking and control of neuronal Hodgkin-Huxley dynamics. Physical Review E 79(4), 040901 (2009) https://doi.org/10.1103/PhysRevE.79.040901 Meliza et al. [2014] Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Meliza, C.D., Kostuk, M., Huang, H., Nogaret, A., Margoliash, D., Abarbanel, H.D.I.: Estimating parameters and predicting membrane voltages with conductance-based neuron models. Biol Cybern 108(4), 495–516 (2014) https://doi.org/10.1007/s00422-014-0615-5 Bieker et al. [2019] Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. 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(ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. 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Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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[2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive control with online learning for complex physical systems (2019) https://doi.org/10.48550/ARXIV.1905.10094 . Publisher: arXiv Version Number: 1 Kaiser et al. [2018] Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. 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Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. 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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Kaiser, E., Kutz, J.N., Brunton, S.L.: Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474(2219), 20180335 (2018) https://doi.org/10.1098/rspa.2018.0335 Salzmann et al. [2023] Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M.: Real-time neural MPC: Deep learning model predictive control for quadrotors and agile robotic platforms. IEEE Robotics and Automation Letters 8(4), 2397–2404 (2023) https://doi.org/10.1109/LRA.2023.3246839 Zheng and Wu [2023] Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Zheng, Y., Wu, Z.: Physics-informed online machine learning and predictive control of nonlinear processes with parameter uncertainty. Industrial & Engineering Chemistry Research 62(6), 2804–2818 (2023) https://doi.org/10.1021/acs.iecr.2c03691 Plaster and Kumar [2019] Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. 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IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Plaster, B., Kumar, G.: Data-driven predictive modeling of neuronal dynamics using long short-term memory. Algorithms 12(10), 203 (2019) https://doi.org/10.3390/a12100203 Sherman-Gold [2012] Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sherman-Gold, R.: The axon guide, a guide to electrophysiology and biophysics laboratory techniques. San Jose: Molecular Devices, LLC (2012) Sterratt [2011] Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. 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IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Sterratt, D. (ed.): Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge ; New York (2011). OCLC: ocn690090171 Destexhe et al. [2001] Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Destexhe, A., Rudolph, M., Fellous, J.-M., Sejnowski, T.J.: Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1), 13–24 (2001) https://doi.org/10.1016/S0306-4522(01)00344-X Knowlton et al. [2014] Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Knowlton, C., Meliza, C.D., Margoliash, D., Abarbanel, H.D.I.: Dynamical estimation of neuron and network properties III: network analysis using neuron spike times. Biological Cybernetics 108(3), 261–273 (2014) https://doi.org/10.1007/s00422-014-0601-y Skinner [2006] Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. 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[2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Skinner, F.K.: Conductance-based models. Scholarpedia 1(11), 1408 (2006) Bourdeau et al. [2019] Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). 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[2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bourdeau, M., Zhai, X.Q., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48, 101533 (2019) https://doi.org/10.1016/j.scs.2019.101533 Clark et al. [2022] Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. 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IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fuller, L., Platt, J.A., Abarbanel, H.D.I.: Reduced-dimension, biophysical neuron models constructed from observed data. Neural Computation 34(7), 1545–1587 (2022) https://doi.org/10.1162/neco_a_01515 Park and Sandberg [1991] Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. 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In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Computation 3(2), 246–257 (1991) https://doi.org/10.1162/neco.1991.3.2.246 Clark et al. [2022] Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. 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Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. 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[2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Clark, R., Fairbanks, L., Sanchez, R., Wacharanan, P., Abarbanel, H.: Data driven regional weather forecasting. preprint, Predictability, probabilistic forecasts, data assimilation, inverse problems/Climate, atmosphere, ocean, hydrology, cryosphere, biosphere/Big data and artificial intelligence (November 2022). https://doi.org/10.5194/egusphere-2022-1222 Lorenz [1963] Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. 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[2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lorenz, E.N.: Deterministic nonperiodic flow. Journal of atmospheric sciences 20(2), 130–141 (1963) Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12, 2825–2830 (2011) Qin and Badgwell [2003] Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11(7), 733–764 (2003) https://doi.org/10.1016/S0967-0661(02)00186-7 Fiedler et al. [2023] Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fiedler, F., Karg, B., Lüken, L., Brandner, D., Heinlein, M., Brabender, F., Lucia, S.: do-mpc: Towards FAIR nonlinear and robust model predictive control. Control Engineering Practice 140, 105676 (2023) https://doi.org/10.1016/j.conengprac.2023.105676 Andersson et al. [2019] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. 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IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. 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[1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36 (2019) https://doi.org/10.1007/s12532-018-0139-4 Wächter and Biegler [2006] Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 106(1), 25–57 (2006) https://doi.org/10.1007/s10107-004-0559-y Mulansky and Kreuz [2016] Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Mulansky, M., Kreuz, T.: PySpike—A Python library for analyzing spike train synchrony. SoftwareX 5, 183–189 (2016) https://doi.org/10.1016/j.softx.2016.07.006 Bottjer et al. [1986] Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. 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IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bottjer, S.W., Miesner, E.A., Arnold, A.P.: Changes in neuronal number, density and size account for increases in volume of song-control nuclei during song development in zebra finches. Neuroscience Letters 67(3), 263–268 (1986) https://doi.org/10.1016/0304-3940(86)90319-8 Milias-Argeitis and Khammash [2015] Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Milias-Argeitis, A., Khammash, M.: Adaptive model predictive control of an optogenetic system. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 1265–1270. IEEE, Osaka (2015). https://doi.org/10.1109/CDC.2015.7402385 Fox et al. [2023] Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Fox, Z.R., Batt, G., Ruess, J.: Bayesian filtering for model predictive control of stochastic gene expression in single cells. Physical Biology 20(5), 055003 (2023) https://doi.org/10.1088/1478-3975/ace094 Bemporad and Morari [1999] Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bemporad, A., Morari, M.: Robust model predictive control: A survey. In: Garulli, A., Tesi, A. (eds.) Robustness in Identification and Control, pp. 207–226. Springer, London (1999) Smith et al. [2010] Smith, A.C., Scalon, J.D., Wirth, S., Yanike, M., Suzuki, W.A., Brown, E.N.: State-space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscience 2010, 1–14 (2010) Langdon et al. [2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. 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[2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. 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[2023] Langdon, C., Genkin, M., Engel, T.A.: A unifying perspective on neural manifolds and circuits for cognition. Nature Reviews Neuroscience, 1–15 (2023) Lambeth et al. [2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. 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[2023] Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. 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IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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[2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. 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IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
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[2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Lambeth, K., Singh, M., Sharma, N.: Robust control barrier functions for safety using a hybrid neuroprosthesis. In: 2023 American Control Conference (ACC), pp. 54–59. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10155862 Wolf and Schearer [2022] Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Wolf, D.N., Schearer, E.M.: Trajectory optimization and model predictive control for functional electrical stimulation-controlled reaching. IEEE Robotics and Automation Letters 7(2), 3093–3098 (2022) https://doi.org/10.1109/LRA.2022.3145946 Singh and Sharma [2023] Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Singh, M., Sharma, N.: Data-driven model predictive control for drop foot correction. In: 2023 American Control Conference (ACC), pp. 2615–2620. IEEE, San Diego, CA, USA (2023). https://doi.org/10.23919/ACC55779.2023.10156600 Bao et al. [2019] Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Bao, X., Kirsch, N., Dodson, A., Sharma, N.: Model predictive control of a feedback-linearized hybrid neuroprosthetic system with a barrier penalty. Journal of Computational and Nonlinear Dynamics 14(10), 101009 (2019) https://doi.org/10.1115/1.4042903 Chatterjee et al. [2020] Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Chatterjee, S., Romero, O., Ashourvan, A., Pequito, S.: Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy. Journal of Neural Engineering 17(6), 066017 (2020) Brar et al. [2018] Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE
- Brar, H.K., Exarchos, I., Pan, Y., Theodorou, E., Mahmoudi, B.: Seizure reduction using model predictive control. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3152–3155 (2018). IEEE